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The Role of AI in Wealth Management

Shreya Jain
14 Oct 2022

During the last few years, Artificial Intelligence (AI) has rapidly established various use cases for itself across different areas of Financial Services (FS).

Increasingly, AI-based applications are being used not only to augment human expertise in routine tasks, but also to streamline the more strategic business processes. One prominent industry area that AI is significantly transforming is Wealth Management (WM). The reason for this is the need for the highest level of accuracy, impeccable precision in analysis, and the sheer volume of data to derive insights from. The WM industry is starting to harness the benefits of AI in ripe use cases, the success of which is, in turn, opening doors to explore newer applications.1

There are many ways for WM firms to leverage AI – the strategies or niches varying by the segments of clients they serve, the investment types they advocate, their overall investment philosophies, and the AI capabilities they possess. Even in the field of Digital Advisory alone, services could range from digital-only advisory, hybrid advisory, or simply augmenting portfolio rebalancing capabilities with AI-derived insights.

As artificial intelligence is poised to enhance the various touchpoints in the wealth landscape, some front-runners that stand to readily benefit are emerging:

  • Portfolio Management: AI can help churn huge chunks of data instantaneously and derive meaningful, context-relevant insights. Financial Institutions (FIs) can leverage this functionality to generate portfolio insights sensitive to dynamic and wider contexts. Robo advisors by FIs like Vanguard and Charles Schwab can build, monitor, and automatically rebalance a diversified portfolio based on the client’s goals.2 3
  • Augmented Advisory and Next Best Action (NBA): With the advent of technologies that facilitate tapping into more and more data on clients, AI can help FIs harness this ever-increasing pool to arrive at bespoke recommendations for each client. Morgan Stanley has developed an NBA system, which leverages machine learning to consider clients’ life events and generate hyper-personalized investment proposals in near-instant time frames.4
  • Tax Planning: Taxation is a vast and important domain for high-net worth individuals, within which AI finds multiple use cases. Right from automated tax filing by appropriately classifying tax-sensitive transactions or recommending investments for tax saving, there is a large scope for both generic and tailor-made AI solutions. Thus, new AI tax solutions focused on different needs are now entering the market. AiTax guarantees that clients pay the lowest amount of tax legally possible, using AI to scan opportunities and eliminate the risk of human error.5 6
  • Client Onboarding: Wealth management firms face Know-Your-Customer (KYC) requirements that are different from the rest of the industry, owing to the more stringent regulatory due diligence required while screening their clients. Artificial intelligence can aid in automating these time- and labour-intensive tasks, while adequately considering contextual relevance. Deutsche Bank Wealth Management is implementing the Finantix KYC Solution, which provides AI-powered multi-language and natural language processing to verify users. It includes the screening of adverse news and background information on existing and potential clients, and builds detailed profiles on them by aggregating, distilling, and classifying them by relevance and risk level.7
  • Cyber Security: With an ever-increasing amount of data being stored on cloud servers, the need to protect the privacy of clients’ financial records and private information falls on wealth management firms. This makes an ideal case for AI software sophisticated, up-to-date, real-time monitoring capabilities to flag issues at first notice. Goldman Sachs has a fund of $72.5 million exclusively for investment in AI, of which a crucial use case is the prevention of cyberattacks using AI-powered anomaly detection software based on real-time data analysis.8

The Wealth Management industry has only just begun to realize the impact of AI. It is still coming to terms with adopting the readily available solutions that aid portfolio managers and help streamline processes. However, the FS landscape is evolving to include advancements such as open banking, better accessibility of third-party market, growing interest in ESG investing, and other dynamic changes. As the landscape changes, wealth management firms stand to discover and invent newer roles for themselves in customer relationship journeys. This emerging definition of WM roles can greatly benefit, by building upon the unprecedented functionality provided by AI solutions, particularly in newer areas, like algorithmic trading and real estate investing.9

With the plethora of AI use cases still available to be piloted, we have only scratched the surface of what could be a transformed wealth management industry.10




Shreya Jain

Manager, Global Banking Industry